INTI Institutional Repository
Not a member yet
2178 research outputs found
Sort by
An Analytical Solution for Sales of Seafood in District Karang Agung Ilir
No full textThe advent of internet technology has profoundly transformed information dissemination and commerce by providing continuous, global access. For Small and Medium Enterprises (UMKM) such as the Seafood District of Karang Agung Ilir—specializing in seafood products like shrimp, crabs, and squid—reliance on traditional market-based sales methods limits customer reach and hampers growth potential. This paper investigates the development of an innovative online-based sales information system aimed at modernizing and significantly improving the operational efficiency of the Seafood District. To evaluate the effectiveness of the proposed system, a System Usability Scale (SUS) test was conducted with 50 participants. The results yielded an average SUS score of 72, indicating that the system offers good usability but also highlights areas requiring further enhancement. The online platform is designed to provide 24/7 accessibility, optimize the sales process, and broaden the customer base beyond the local market. The anticipated advantages of this digital transition include reduced operational costs, enhanced customer service, and increased sales volume. By adopting online technology, the Seafood District of Karang Agung Ilir stands to strengthen its market presence and boost its competitiveness on a global scale
Customer Service Management on Enhancing Customer Loyalty in Food and Beverage Industry: Case Study of Starbucks Malaysia
No full textIn the highly competitive coffee industry, the operation of a successful coffee shop involves more than just providing a simple cup of coffee. Organizations are now recognizing the need to focus on delivering high-quality service to gain a competitive advantage. Emphasizing service quality is crucial for building customer loyalty. This paper aims to demonstrate and investigate the service quality using the SERVQUAL model, specifically in the context of Malaysia's Starbucks outlets. To gather real-time data, this paper employs a questionnaire-based survey. A total of 221 questionnaires were distributed to the public. Following data collection, the obtained data is analyzed using SPSS software. Additionally, this paper offers insights into the current state of Starbucks in Malaysia and proposes practical actions to enhance service quality. The objective is to not only attract and retain customers but also cultivating customer loyalty within the competitive coffee chain industry. By focusing on the SERVQUAL model, this research seeks to identify the key dimensions of service quality that significantly impact customer loyalty. These insights are essential for Starbucks to make informed decisions and implement effective strategies to elevate its service quality
Predictive Analytics in Genetic Engineering as an Optimization Problem
No full textIn genetic engineering, developing a breed with a desired trait is a search and optimization problem that sometimes requires many generations of field and laboratory experiments for an optimal solution to be found. The nature of the problem requires that a stochastic optimization algorithm be applied in the metaheuristic search rather than using a deterministic or mathematical approach. In the search for drought-tolerant cowpea, this study applied a genetic algorithm as a predictive analytics tool in the genetic engineering of three native cowpea landraces (Dan muzakkari, Gidigiwa, and Dan mesera) selected from Northern Nigeria (specifically from Kontagora in Niger State of Nigeria). The three cowpea species were subjected to mutagenic treatments using gamma irradiation and Ethyl Methane Sulphonate (EMS). Doses applied include 200, 400, 600, and 800 Gray of gamma irradiation and 0.372% v/v of EMS. Both treated and untreated cowpea landraces were planted and observed. Mutation-induced breeding aims to deepen the drought-tolerant trait of the cowpea mutants to survive conditions in drought-prone Northern Nigeria. The statistical analysis of the agro-morphological and yield parameters of the first mutant generation (M1 generation) indicates that mutagenic treatments have a positive impact on both the yield and the survival of the three landraces as all the treated landraces yielded better than the control, particularly the treatments combination of 600gray and 372% v/v of EMS. Also, the predictive outcomes of the computational simulation that was implemented in Python programming indicate that these local cultivars are developing drought-tolerant genetic variability. For the three computational experiments, the stochastic optimizer (genetic algorithm) converged at the 9412th, 9717th, and 14338th generations respectively. Such predictive analytics information is useful for guiding decision-making by researchers and breeders in the crop improvement program
AI Solutions for Accessible Education in Underserved Communities
No full textThis paper explores the application of artificial intelligence technology in the field of education, particularly how it can help bridge educational gaps in remote and underserved communities through scalable and accessible learning solutions. The aim of the study is to enhance educational equity and provide personalized learning experiences by utilizing AI technologies such as adaptive learning systems, language processing technologies, and data analytics. The paper analyzes these tools and discusses how they integrate with practical cases like mobile learning platforms, cloud infrastructure, open resources, and collaborative learning to massively distribute educational resources and address global educational inequalities. The research methods include case studies and data analysis, with results indicating that these technologies significantly improve learning efficiency and engagement among students in remote areas. Ultimately, the paper demonstrates the potential of artificial intelligence in promoting global educational equity and offers suggestions for the future development of educational technology. This work is of significant importance to the field of educational technology, providing innovative perspectives and practical solutions for addressing disparities in educational resources
The Intention of Community Garden Participation: A Case Study in Community Garden of Taman Tasik Ilmu, Kota Seriemas
No full textMalaysia's population is projected to reach approximately 41.5 million by 2040, marking a significant
increase from its current population. This projection is based on various factors, including birth rates,
death rates, and migration trends. With this rapid population increase, the urbanization rate is
expected to rise, leading to concerns about food security. Ensuring food security becomes a pressing
issue, as the country must produce or import enough food to feed its population. This is where
community garden programs and other local food production initiatives can play a significant role in
supplementing food supply and promoting sustainable agriculture. The United Nations' Sustainable
Development Goals (SDGs) introduced in 2015, specifically Goal No. 2 (Zero Hunger), emphasize
the need to address food security issues. One of Malaysia's strategies to achieve Goal No. 2 is through
the implementation of community garden programs. However, the success of such programs largely
depends on the intention and volunteerism of community participants. This study aims to explore the
intention behind community garden participation at Taman Tasik Ilmu. Data was collected using an
online questionnaire with 40 participants. The questionnaire gathered information on demographics,
gardening knowledge, reasons for participation, and opinions on facilities and garden management.
Descriptive analysis was employed to analyze the collected data. The results indicated that the
primary motivation for participants to join the community garden program was to fill their free time.
This finding suggests that aligning the objectives of community garden programs with the intentions
of participants may enhance their success and sustainability
Design and Construction of an Arduino-Driven Weight-Based Fish Sorting Tool
No full textThis research aims to design and develop a weight-based fish sorting tool that uses a strain gauge
sensor under Arduino Uno control. We designed this tool to enhance the effectiveness and
accuracy of the fish sorting process, focusing on three size categories: small, medium, and large.
The system consists of a strain gauge sensor that measures the weight of the fish, an HX711 module
that converts analog data to digital, a servo motor to move the fish, and a conveyor that moves the
fish through the sorting process. We conducted testing to measure the accuracy of the system in
determining the fish's weight and its success in classifying it based on its size. The test results
show that this device has an average accuracy rate of 99.5%. However, minor differences in weight
measurements suggest the need for further improvements. Overall, the fishing industry has
enormous potential to implement this tool to increase productivity and reduce reliance on manual
methods
Automated Bird Species Identification Through Machine Learning Techniques
No full textThe taxonomy of bird species is fundamental to ecological research, conservation efforts, and
biodiversity monitoring. Traditional identification methods, which rely on field notes and
visual assessments by trained ornithologists, are often labor-intensive, time-consuming, and
prone to error. In recent years, machine learning algorithms and pre-trained models such as
ResNet, Histogram of Oriented Gradients (HOG), and Scale-Invariant Feature Transform
(SIFT) have shown significant promise in automating bird species classification. This study
explores the application of these advanced models in identifying bird species from visual data,
discussing key challenges, methodologies, and the potential to achieve high classification
accuracy with reliable confidence scores. By leveraging deep learning techniques, we aim to
enhance the precision and scalability of bird taxonomy, supporting more efficient ecological
studies and conservation practices
Role and Impact of Safety Leadership Culture in Promoting Safety Practices in Construction Sector of Pakistan
No full textThe construction industry offers employment to about one hundred eighty million people
worldwide. The construction sector has grown and evolved to meet the demands of urbanization
and industrialization, which are the foundation of the modern economy. Construction is still seen
as a risky and difficult industry, with the most hazardous and vulnerable work environment, even
with the industry's notable improvements. Pakistan's construction industry is known for its high
level of risk, which makes strict safety procedures necessary to protect the health and safety of its
workforce. This research seeks to understand the impact of safety leadership on safety practices
and identify key factors influencing safety culture. In order to provide comprehensive insights, the
research uses a mixed-methods approach that combines quantitative surveys and qualitative
interviews. The validity and reliability of the results are ensured by thorough experimentations of
the statistical methods such as reliability test, relative importance index (RII), and descriptive
analysis tests. In order to effectively promote safety practices, management commitment,
employee involvement, safety training, and good communication are essential. An in-depth
examination of the relationship between management commitment and safety performance reveals
how effective leadership and proactive participation improve safety results. The results shows that
safety leadership is crucial to encourage safety procedures in Pakistan's construction sector by
68.81%, success factor for safety management by 65.42%, safety management practices by
64.32%, safety performance by 63.35%, barriers to effective safety management 62.47% and
safety culture by 61.46%. Strengthening frameworks for regulations, promoting a safety-first
culture, investing more in safety equipment, improving worker safety training, improving
monitoring and evaluation mechanisms, fostering communication and collaboration, and
addressing cultural barriers are just a few of the recommendations
Analyzing the Impact of Entrepreneurial Orientation on Sustainable Business Performance using Hierarchical Linear Regression
No full textThis research explores the relationship between Entrepreneurial Orientation (EO) and Sustainable
business performance (SBP). EO is a strategic posture characterized by innovativeness,
proactiveness, risk-taking, autonomy and competitiveness which are hypothesized to influence
SBP positively. The study aims to explore how these EO dimensions contribute to achieving
sustainable outcomes across various industries. Data for the analysis is collected from a diverse
sector which includes manufacturing and service focusing on their strategic orientations and their
sustainability policies. Data was collected from the senior and mid level managers of 150 small
and medium scale companies in Chennai. Proposed hypotheses were tested using hierarchical
linear regression analysis. Understanding the impact of EO along with Technology capabilities on
SBP can provide insights into how firms can effectively leverage entrepreneurial behaviors to
enhance their sustainability performance. For practitioners, the research highlights specific areas—
such as fostering innovation, proactive environmental management, and calculated risk-taking—
that can lead to improved sustainability outcomes
Breast Cancer Detection Using Image Processing and Machine Learning
No full textAs the outlines picturize, one driving reason for death in women across the entire world is
breast cancer. It is an often-occurring disease in women, affecting approximately 2.1 million
women annually. Studies indicate it generally affects women more in developed regions,
although rates are increasing globally. While prevention may not be a feasible option,
improving the outcomes and survival rates of breast cancer is a viable goal. Breast cancer
mortality can be considerably decreased by more efficient treatments, which are made possible
by early discovery of the disease. Many researchers and scientists are working on methods to
facilitate early detection of breast cancer. Using the K-Nearest Neighbors (KNN) algorithm is
one such technique. KNN is a straightforward machine learning technique that works well for
regression and classification. In order to categorize an input according to the majority class of
its neighbors, it first finds the k-nearest data points to the input. Using features taken from
medical imaging, KNN can be utilized to determine a tumor's malignancy or benignity in the
context of breast cancer detection. This algorithm is a useful tool for creating precise and
dependable diagnostic systems since it can adjust and get better with additional data